Towards a computer aided diagnosis system dedicated to virtual microscopy based on stereology sampling and diffusion maps

An original strategy is presented, combining stereological sampling methods based on test grids and data reduction methods based on diffusion maps, in order to build a knowledge image database with no bias introduced by a subjective choice of exploration areas. The practical application of the exposed methodology concerns virtual slides of breast tumors

Automatic morphological sieving: comparison between different methods, application to DNA ploidy measurements

The aim of the present study is to propose alternative automatic methods to time consuming interactive sorting of elements for DNA ploidy measurements. One archival brain tumour and two archival breast carcinoma were studied, corresponding to 7120 elements (3764 nuclei, 3356 debris and aggregates). Three automatic classification methods were tested to eliminate debris and aggregates from DNA ploidy measurements (mathematical morphology (MM), multiparametric analysis (MA) and neural network (NN)). Performances were evaluated by reference to interactive sorting. The results obtained for the three methods concerning the percentage of debris and aggregates automatically removed reach 63, 75 and 85% for MM, MA and NN methods, respectively, with false positive rates of 6, 21 and 25%. In-* Corresponding author. formation about DNA ploidy abnormalities were globally preserved after automatic elimination of debris and aggregates by MM and MA methods as opposed to NN method, showing that automatic classification methods can offer alternatives to tedious interactive elimination of debris and aggregates, for DNA ploidy measurements of archival tumours

Impact of tissue sampling on accuracy of Ki67 immunohistochemistry evaluation in breast cancer

Background: Gene expression studies have identified molecular subtypes of breast cancer with implications to chemotherapy recommendations. For distinction of these types, a combination of immunohistochemistry (IHC) markers, including proliferative activity of tumor cells, estimated by Ki67 labeling index is used. Clinical studies are frequently based on IHC performed on tissue microarrays (TMA) with variable tissue sampling. This raises the need for evidence-based sampling criteria for individual IHC biomarker studies. We present a novel tissue sampling simulation model and demonstrate its application on Ki67 assessment in breast cancer tissue taking intratumoral heterogeneity into account.Methods: Whole slide images (WSI) of 297 breast cancer sections, immunohistochemically stained for Ki67, were subjected to digital image analysis (DIA). Percentage of tumor cells stained for Ki67 was computed for hexagonal tiles super-imposed on the WSI. From this, intratumoral Ki67 heterogeneity indicators (Haralick’s entropy values) were extracted and used to dichotomize the tumors into homogeneous and heterogeneous subsets. Simulations with random selection of hexagons, equivalent to 0.75 mm circular diameter TMA cores, were performed. The tissue sampling requirements were investigated in relation to tumor heterogeneity using linear regression and extended error analysis.Results: The sampling requirements were dependent on the heterogeneity of the biomarker expression. To achieve a coefficient error of 10 %, 5–6 cores were needed for homogeneous cases, 11–12 cores for heterogeneous cases; in mixed tumor population 8 TMA cores were required. Similarly, to achieve the same accuracy, approximately 4,000 nuclei must be counted when the intratumor heterogeneity is mixed/unknown. Tumors of low proliferative activity would require larger sampling (10–12 TMA cores, or 6,250 nuclei) to achieve the same error measurement results as for highly proliferative tumors.Conclusions: Our data show that optimal tissue sampling for IHC biomarker evaluation is dependent on the heterogeneity of the tissue under study and needs to be determined on a per use basis. We propose a method that can be applied to determine the sampling strategy for specific biomarkers, tissues and study targets. In addition, our findings highlight the benefit of high-capacity computer-based IHC measurement techniques to improve accuracy of the testing

Bimodality of intratumor Ki67 expression is an independent prognostic factor of overall survival in patients with invasive breast carcinoma

Proliferative activity, assessed by Ki67 immunohistochemistry (IHC), is an established prognostic and predictive biomarker of breast cancer (BC). However, it remains under-utilized due to lack of standardized robust measurement methodologies and significant intratumor heterogeneity of expression. A recently proposed methodology for IHC biomarker assessment in whole slide images (WSI), based on systematic subsampling of tissue information extracted by digital image analysis (DIA) into hexagonal tiling arrays, enables computation of a comprehensive set of Ki67 indicators, including intratumor variability. In this study, the tiling methodology was applied to assess Ki67 expression in WSI of 152 surgically removed Ki67-stained (on full-face sections) BC specimens and to test which, if any, Ki67 indicators can predict overall survival (OS). Visual Ki67 IHC estimates and conventional clinico-pathologic parameters were also included in the study. Analysis revealed linearly independent intrinsic factors of the Ki67 IHC variance: proliferation (level of expression), disordered texture (entropy), tumor size and Nottingham Prognostic Index, bimodality, and correlation. All visual and DIA-generated indicators of the level of Ki67 expression provided significant cutoff values as single predictors of OS. However, only bimodality indicators (Ashman’s D, in particular) were independent predictors of OS in the context of hormone receptor and HER2 status. From this, we conclude that spatial heterogeneity of proliferative tumor activity, measured by DIA of Ki67 IHC expression and analyzed by the hexagonal tiling approach, can serve as an independent prognostic indicator of OS in BC patients that outperforms the prognostic power of the level of proliferative activity

Contribution à l étude du stroma des carcinomes ovariens par une analyse morphologique, morphométrique et expérimentale, à l échelle histologique et ultrastructurale (mise en évidence de l importance de la vascularisation)

CAEN-BU Médecine pharmacie (141182102) / SudocLYON1-BU Santé (693882101) / SudocSudocFranceF

Contribution à l'étude du stroma des tumeurs ovariennes humaines, par traitement et analyse d'images numériques

La recherche de marqueurs pronostiques et de suivi thérapeutique est capitale dans le contexte d'un carcinome ovarien; la quantité de stroma nourricier est un marqueur potentiel. Pour s'affranchir des problèmes liées à l'hétérogénéité tumorale et à la quantification à l'échelle microscopique, un protocole d'étude du stroma et de la prolifération à basse résolution a été mis en place. Cette thèse a mis en lumière la valeur prédictive de la quantification du compartiment stromal et de la néovascularisation ainsi qu'une relation inverse entre la prolifération du compartiment carcinomateux et la quantité de stromaCAEN-BU Médecine pharmacie (141182102) / SudocLYON1-BU Santé (693882101) / SudocSudocFranceF

Approches multiéchelles pour la segmentation de très grandes images ( application à la quantification de biomarqueurs en histopathologie cancérologique)

Visualiser et analyser automatiquement des coupes fines de tumeurs cancéreuses sont des enjeux majeurs pour améliorer la compréhension des mécanismes de la cancérisation. Les scanners microscopiques haute résolution fournissent des lames virtuelles (de plusieurs Gigaoctets) de la totalité de la lame histologique. Ceci permet de s'affranchir de l'hétérogénéité de distribution des marqueurs à quantifier. Le but de cette thèse est de concevoir une méthode de segmentation des différents types de stroma d'une lame virtuelle de carcinome ovarien. Les obstacles sont la taille des images et le choix de critères permettant de différencier les compartiments stromaux. Pour les contourner, nous proposons une méthode générique de segmentation multiéchelle qui associe un découpage judicieux de l'image à une caractérisation des compartiments considérés comme des textures. Celle-ci repose sur une modélisation multiéchelle des textures par un modèle d'arbre de Markov caché, appliqué aux coefficients de la décomposition en ondelettes. Plutôt que de considérer toutes les classes simultanément, nous avons transformé le problème en un ensemble de problèmes binaires. L'analyse de l'influence d'hyperparamètres sur la segmentation nous a permis de sélectionner les classifieurs les mieux adaptés. Différentes méthodes de combinaison des décisions des meilleurs classifieurs ont ensuite été étudiées. La méthode a été testée sur une vingtaine de lames virtuelles. Les résultats obtenus sont prometteurs, compte tenu de la variabilité des échantillons et de la difficulté à, parfois, identifier très précisément un compartiment. Environ 60% des points sont correctement classés (de 35 à 80% selon la lame).Viewing and analyzing automatically sections of cancer tissue are major challenges to progress in the understanding of cancer development and to discover new indicators of response to therapy. The new microscopical scanners provide an essential assistance by supplying high-resolution color virtual slides of the whole histological slide that can reach several gigabytes. This allows us to overcome the issue of the distribution heterogeneity of the markers which have to be quantified. The aim of this thesis is to design a method in order to segment the various stromal compartments on an ovarian carcinoma virtual slide. The difficulties to overcome are the size of images and the choice of criteria to differentiate the compartments. To tackle these problems, we developed a generic segmentation framework which combines a smart split of the image to a characterization of each compartment, regarded as a texture. This characterization is based on a multiscale modeling of textures thanks to a hidden Markov tree model, applied to the wavelet decomposition coefficients. Rather than considering all classes of compartments at once, the multiclass problem was transformed into a set of binary problems. The influence of hyperparameters on segmentation was also analyzed. This allowed us to select the most appropriate classifiers. Several methods of combination of the best classifier decisions were then studied. The method was tested on more than twenty virtual slides. The results are promising, notably if we consider the variability of samples and the difficulty to identify precisely a compartment: about 60% of points are well classified (between 35 % and 80 % according to the slide).CAEN-BU Sciences et STAPS (141182103) / SudocSudocFranceF

Indexation automatique d'images numériques ( application aux images histopathologiques du cancer du sein et hématologiques de leucémies lympoïdes chroniques)

Dans un contexte où les économies de santé sont de plus en plus drastiques, où les spécialistes sont de moins en moins nombreux, alors que, grâce aux campagnes de dépistage, le nombre de cas à analyser est en constante augmentation, le travail des pathologistes devient de plus en plus difficile. En outre, les lésions précoces découvertes lors du dépistage sont souvent mal connues et/ou de très petite taille ce qui rend délicat le diagnostic histopathologique. Un problème similaire est rencontré en hématologie avec la pratique de plus en plus répandue des examens sanguins systématiques et la difficulté d'identification de cellules suspectes et d'évènements rares au sein d'un frottis sanguin. Il est par conséquent très important d apprécier dans quelle mesure la microscopie numérique et les outils d analyse automatique des images pourront dans l avenir aider ces spécialistes dans l accomplissement de leur tâche quotidienne. Le présent travail de thèse, mené dans cette optique, se fonde sur l'utilisation de lames virtuelles des préparations histologiques et cytologiques, acquises à basse ou à haute résolution. Il consiste à développer et à tester une série d'outils d'aide au diagnostic basés sur l indexation automatique des images. Cependant, l utilisation des lames virtuelles implique la manipulation d'une masse de données très importante, qui constitue un frein pour traiter, analyser et même visualiser les images de manière classique. Nous avons donc testé tout d abord la pertinence d'une analyse globale des images, puis d'une analyse locale de celles-ci, accompagnées d une réduction de dimension des données par diverses méthodes, dont l'analyse spectrale.Nous avons choisi de mettre en œuvre cette approche à propos de deux localisations dont l incidence constitue un problème de santé publique, les tumeurs mammaires et la leucémie lymphoïde chronique.In a context where health economies are increasingly curbed, where specialists are fewer and fewer, while, thanks to screening campaigns, the number of cases to analyze is constantly growing, the task of pathologists is more and more difficult. In addition, early lesions discovered during the screening are often poorly known and / or of very small size which makes the histopathological diagnosis difficult. A similar problem is encountered in hematology with the increasingly widespread practice of systematic blood tests and the difficulty of identifying suspicious cells and rare events in blood smears. It is therefore very important to assess how digital microscopy and automatic processing techniques will be able to help the specialists in their daily practice in the future. This present work is based on the use of virtual slides of histological and cytological preparations, acquired at low or high resolution. It aims at developing and testing several tools for computer assisted diagnosis based on automatic indexing of images. However, the use of virtual slides involves the manipulation of very large data and it is difficult to process, analyze or visualize these images in a classical way. The first objective of the study was to assess the relevance of a global analysis of images, then the contribution of their local analysis, with a dimensional reduction of data by various methods including spectral analysis. These methods have been applied to virtual slides of breast tumors and chronic lymphoid leukemia, two tumor locations whose incidence is a public health problem.CAEN-BU Sciences et STAPS (141182103) / SudocSudocFranceF